Browsing by Author "Curry, Kristen"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Analysis of bronchoalveolar lavage fluid metatranscriptomes among patients with COVID-19 disease
    (Springer Nature, 2022) Jochum, Michael; Lee, Michael D.; Curry, Kristen; Zaksas, Victoria; Vitalis, Elizabeth; Treangen, Todd; Aagaard, Kjersti; Ternus, Krista L.
    To better understand the potential relationship between COVID-19 disease and hologenome microbial community dynamics and functional profiles, we conducted a multivariate taxonomic and functional microbiome comparison of publicly available human bronchoalveolar lavage fluid (BALF) metatranscriptome samples amongst COVID-19 (n = 32), community acquired pneumonia (CAP) (n = 25), and uninfected samples (n = 29). We then performed a stratified analysis based on mortality amongst the COVID-19 cohort with known outcomes of deceased (n = 10) versus survived (n = 15). Our overarching hypothesis was that there are detectable and functionally significant relationships between BALF microbial metatranscriptomes and the severity of COVID-19 disease onset and progression. We observed 34 functionally discriminant gene ontology (GO) terms in COVID-19 disease compared to the CAP and uninfected cohorts, and 21 GO terms functionally discriminant to COVID-19 mortality (q < 0.05). GO terms enriched in the COVID-19 disease cohort included hydrolase activity, and significant GO terms under the parental terms of biological regulation, viral process, and interspecies interaction between organisms. Notable GO terms associated with COVID-19 mortality included nucleobase-containing compound biosynthetic process, organonitrogen compound catabolic process, pyrimidine-containing compound biosynthetic process, and DNA recombination, RNA binding, magnesium and zinc ion binding, oxidoreductase activity, and endopeptidase activity. A Dirichlet multinomial mixtures clustering analysis resulted in a best model fit using three distinct clusters that were significantly associated with COVID-19 disease and mortality. We additionally observed discriminant taxonomic differences associated with COVID-19 disease and mortality in the genus Sphingomonas, belonging to the Sphingomonadacae family, Variovorax, belonging to the Comamonadaceae family, and in the class Bacteroidia, belonging to the order Bacteroidales. To our knowledge, this is the first study to evaluate significant differences in taxonomic and functional signatures between BALF metatranscriptomes from COVID-19, CAP, and uninfected cohorts, as well as associating these taxa and microbial gene functions with COVID-19 mortality. Collectively, while this data does not speak to causality nor directionality of the association, it does demonstrate a significant relationship between the human microbiome and COVID-19. The results from this study have rendered testable hypotheses that warrant further investigation to better understand the causality and directionality of host–microbiome–pathogen interactions.
  • Thumbnail Image
    Item
    Fecal Microbiota Transplantation Derived from Alzheimer’s Disease Mice Worsens Brain Trauma Outcomes in Wild-Type Controls
    (MDPI, 2022) Soriano, Sirena; Curry, Kristen; Wang, Qi; Chow, Elsbeth; Treangen, Todd J.; Villapol, Sonia
    Traumatic brain injury (TBI) causes neuroinflammation and neurodegeneration, both of which increase the risk and accelerate the progression of Alzheimer’s disease (AD). The gut microbiome is an essential modulator of the immune system, impacting the brain. AD has been related with reduced diversity and alterations in the community composition of the gut microbiota. This study aimed to determine whether the gut microbiota from AD mice exacerbates neurological deficits after TBI in control mice. We prepared fecal microbiota transplants from 18 to 24 month old 3×Tg-AD (FMT-AD) and from healthy control (FMT-young) mice. FMTs were administered orally to young control C57BL/6 (wild-type, WT) mice after they underwent controlled cortical impact (CCI) injury, as a model of TBI. Then, we characterized the microbiota composition of the fecal samples by full-length 16S rRNA gene sequencing analysis. We collected the blood, brain, and gut tissues for protein and immunohistochemical analysis. Our results showed that FMT-AD administration stimulates a higher relative abundance of the genus Muribaculum and a decrease in Lactobacillus johnsonii compared to FMT-young in WT mice. Furthermore, WT mice exhibited larger lesion, increased activated microglia/macrophages, and reduced motor recovery after FMT-AD compared to FMT-young one day after TBI. In summary, we observed gut microbiota from AD mice to have a detrimental effect and aggravate the neuroinflammatory response and neurological outcomes after TBI in young WT mice.
  • Thumbnail Image
    ItemEmbargo
    The Microbiome in its Entirety: Community-Oriented Computational Tools for Deciphering Metagenomic Diversity
    (2024-04-19) Curry, Kristen; Treangen, Todd J
    Microbiome. An ecosystem composed of microscopic organisms. Although unseen by the naked eye, these communities can have powerful impacts on their hosts and surrounding environments. Yet, we are just beginning to crack the surface as to who these tiny critters are, how they are surviving, and what their overarching purpose is in the tree of life. This thesis presents software methods developed to improve understanding of these communities by leveraging the advent of high-throughput sequencing and viewing each ecosystem holistically, motivated by the intention of improving upon methods for gut microbiome analysis in concussion recovery. We dive into three computational tools developed for improvement of understanding the diversity within microbial communities: Emu for taxonomic community profiling, Rhea for structural variant detection, and Kiwi for P4 phage satellite detection. Each of these algorithms was designed with the view of the microbiome as a single evolving entity, rather than a sum of unique individuals. Viewing microbiomes through this lens and incorporating computer science theories in expectation-maximization, graph motifs extraction, and sub-string minimizers allowed us to develop software for each of these concepts that showed improvement upon existing methods.